Dimethyl fumarate suppresses granulocyte macrophage colony-stimulating factor-producing Th1 cells in CNS neuroinflammation.

Objective To study the immunomodulatory effect of dimethyl fumarate (DF) on granulocyte macrophage colony-stimulating factor (GM-CSF) production in CD4+ T cells in experimental autoimmune encephalomyelitis (EAE) and human peripheral blood mononuclear cells (PBMCs). Methods We collected splenocytes and CD4+ T cells from C57BL/6 wild-type and interferon (IFN)-γ–deficient mice. For human PBMCs, venous blood was collected from healthy donors, and PBMCs were collected using the Percoll gradient method. Cells were cultured with anti-CD3/28 in the presence/absence of DF for 3 to 5 days. Cells were stained and analyzed by flow cytometry. Cytokines were measured by ELISA in cell supernatants. For in vivo experiments, EAE was induced by myelin oligodendrocyte glycoprotein35–55 and mice were treated with oral DF or vehicle daily. Results DF acts directly on CD4+ T cells and suppresses GM-CSF–producing Th1 not Th17 or single GM-CSF+ T cells in EAE. In addition, GM-CSF suppression depends on the IFN-γ pathway. We also show that DF specifically suppresses Th1 and GM-CSF–producing Th1 cells in PBMCs from healthy donors. Conclusions We suggest that DF exclusively suppresses GM-CSF–producing Th1 cells in both animal and human CD4+ T cells through an IFN-γ–dependent pathway. These findings indicate that DF has a better therapeutic effect on patients with Th1-dominant immunophenotype. However, future longitudinal study to validate this finding in MS is needed

Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases

Esophageal Squamous Cell Carcinoma With Pancreatic Metastasis: A Case Report

Malignant tumors of pancreas are usually primary neoplasms and pancreatic metastases are rare findings. We are reporting a case of squamous cell carcinoma (SCC) of the esophagus with pancreatic metastasis. A 59-year old woman was admitted with chief complaint of abdominal pain and mass. She was a known case of esophageal SCC since 4 years before when she had undergone transthoracic esophagectomy and cervical esophago-gastrostomy. In order to evaluate recent abdominal mass, CT scan was done which revealed septated cystic lesion in the body and the tail of the pancreas. Palliative resection of the tumor was performed and its histological study showed SCC compatible with her previously diagnosed esophageal cancer

Astrocytic junctional adhesion molecule-A regulates T-cell entry past the glia limitans to promote central nervous system autoimmune attack

International audienceAbstract Contact-mediated interactions between the astrocytic endfeet and infiltrating immune cells within the perivascular space are underexplored, yet represent potential regulatory check-points against CNS autoimmune disease and disability. Reactive astrocytes upregulate junctional adhesion molecule-A, an immunoglobulin-like cell surface receptor that binds to T cells via its ligand, the integrin, lymphocyte function-associated antigen-1. Here, we tested the role of astrocytic junctional adhesion molecule-A in regulating CNS autoinflammatory disease. In cell co-cultures, we found that junctional adhesion molecule-A-mediated signalling between astrocytes and T cells increases levels of matrix metalloproteinase-2, C–C motif chemokine ligand 2 and granulocyte-macrophage colony-stimulating factor, pro-inflammatory factors driving lymphocyte entry and pathogenicity in multiple sclerosis and experimental autoimmune encephalomyelitis, an animal model of CNS autoimmune disease. In experimental autoimmune encephalomyelitis, mice with astrocyte-specific JAM-A deletion (mGFAP:CreJAM-Afl/fl) exhibit decreased levels of matrix metalloproteinase-2, reduced ability of T cells to infiltrate the CNS parenchyma from the perivascular spaces and a milder histopathological and clinical course of disease compared with wild-type controls (JAM-Afl/fl). Treatment of wild-type mice with intraperitoneal injection of soluble junctional adhesion molecule-A blocking peptide decreases the severity of experimental autoimmune encephalomyelitis, highlighting the potential of contact-mediated astrocyte–immune cell signalling as a novel translational target against neuroinflammatory disease

Decoding clinical biomarker space of COVID-19: Exploring matrix factorization-based feature selection methods

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.</p